Publications

According to World Health Organization statistics of 2011, infectious diseases remain in the top five causes of mortality worldwide. However, despite sophisticated research tools for microbial detection, rapid and accurate molecular diagnostics for identification of infection in humans have not been extensively adopted. Time-consuming culture-based methods remain to the forefront of clinical microbial detection. The 16S rRNA gene, a molecular marker for identification of bacterial species, is ubiquitous to members of this domain and, thanks to ever-expanding databases of sequence information, a useful tool for bacterial identification. In this study, we assembled an extensive repository of clinical isolates (n = 617), representing 30 medically important pathogenic species and originally identified using traditional culture-based or non-16S molecular methods. This strain repository was used to systematically evaluate the ability of 16S rRNA for species level identification. To enable the most accurate species level classification based on the paucity of sequence data accumulated in public databases, we built a Naïve Bayes classifier representing a diverse set of high-quality sequences from medically important bacterial organisms. We show that for species identification, a model-based approach is superior to an alignment based method. Overall, between 16S gene based and clinical identities, our study shows a genus-level concordance rate of 96% and a species-level concordance rate of 87.5%. We point to multiple cases of probable clinical misidentification with traditional culture based identification across a wide range of gram-negative rods and gram-positive cocci as well as common gram-negative cocci.

CONTEXT

Short sleep duration is associated with an increased risk of type 2 diabetes. Subchronic sleep restriction (SR) causes insulin resistance, but the mechanisms and roles of specific tissues are unclear.

OBJECTIVE

The purpose of this article was to determine whether subchronic SR altered (1) hepatic insulin sensitivity, (2) peripheral insulin sensitivity, and (3) substrate utilization.

DESIGN

This was a randomized crossover study in which 14 subjects underwent 2 admissions separated by a washout period. Each admission had 2 acclimatization nights followed by 5 nights of either SR (4 hours time in bed) or normal sleep (8 hours time in bed). MAIN OUTCOME MEASURE/METHODS: Insulin sensitivity (measured by hyperinsulinemic-euglycemic clamp) and hepatic insulin sensitivity (measured by stable isotope techniques) were measured. In addition, we assayed stress hormone (24-hour urine free cortisol, metanephrine, and normetanephrine), nonesterified fatty acid (NEFA), and β-hydroxybutyrate (β-OH butyrate) levels. Resting energy expenditure (REE) and respiratory quotient (RQ) were measured by indirect calorimetry.

RESULTS

Compared to normal sleep, whole-body insulin sensitivity decreased by 25% (P = .008) with SR and peripheral insulin sensitivity decreased by 29% (P = .003). Whereas hepatic insulin sensitivity (endogenous glucose production) did not change significantly, percent gluconeogenesis increased (P = .03). Stress hormones increased modestly (cortisol by 21%, P = .04; metanephrine by 8%, P = .014; normetanephrine by 18%, P = .002). Fasting NEFA and β-OH butyrate levels increased substantially (62% and 55%, respectively). REE did not change (P = 0.98), but RQ decreased (0.81 ± .02 vs 0.75 ± 0.02, P = .045).

CONCLUSION

Subchronic SR causes unique metabolic disturbances characterized by peripheral, but not hepatic, insulin resistance; this was associated with a robust increase in fasting NEFA levels (indicative of increased lipolysis), decreased RQ, and increased β-OH butyrate levels (indicative of whole-body and hepatic fat oxidation, respectively). We postulate that elevated NEFA levels are partially responsible for the decrease in peripheral sensitivity and modulation of hepatic metabolism (ie, increase in gluconeogenesis without increase in endogenous glucose production). Elevated cortisol and metanephrine levels may contribute to insulin resistance by increasing lipolysis and NEFA levels.

Canonical T cell receptor signal transduction has been extensively studied and dissected in cell lines and primary lymphocytes. However, a static depiction of this signaling cascade fails to capture the complex and dynamic process by which individual T cells discriminate TCR:peptide-MHC affinity, then integrate signals over time to drive discrete cellular behaviors such as thymic selection, proliferation, and cytokine production. Recent technological advances have made it possible to study complex lymphocyte behavior on a single cell level and are revealing how T cells interpret information about affinity and abundance of antigen in order to make life-and-death cell fate decisions individually and collectively.